IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i1p127-d125898.html
   My bibliography  Save this article

Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation

Author

Listed:
  • Oseweuba Valentine Okoro

    (Department of Physics, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

  • Zhifa Sun

    (Department of Physics, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

  • John Birch

    (Department of Food Science, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

Abstract

In response to existing global focus on improved biodiesel production methods via highly efficient catalyst-free high temperature and high pressure technologies, this study considered the comparative study of catalyst-free technologies for biodiesel production as an important research area. In this study, therefore, catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification and catalyst-free one step supercritical transesterification processes for biodiesel production have been evaluated via undertaking straight forward comparative energetic and environmental assessments. Energetic comparisons were undertaken after heat integration was performed since energy reduction has favourable effects on the environmental performance of chemical processes. The study confirmed that both processes are capable of producing biodiesel of high purity with catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification characterised by a greater energy cost than catalyst-free one step supercritical transesterification processes for an equivalent biodiesel productivity potential. It was demonstrated that a one-step supercritical transesterification for biodiesel production presents an energetically more favourable catalyst-free biodiesel production pathway compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. The one-step supercritical transesterification for biodiesel production was also shown to present an improved environmental performance compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. This is because of the higher potential environment impact calculated for the integrated subcritical lipid hydrolysis and supercritical esterification compared to the potential environment impact calculated for the supercritical transesterification process, when all material and energy flows are considered. Finally the major contributors to the environmental outcomes of both processes were also clearly elucidated.

Suggested Citation

  • Oseweuba Valentine Okoro & Zhifa Sun & John Birch, 2018. "Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:127-:d:125898
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/1/127/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/1/127/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rincón, L.E. & Jaramillo, J.J. & Cardona, C.A., 2014. "Comparison of feedstocks and technologies for biodiesel production: An environmental and techno-economic evaluation," Renewable Energy, Elsevier, vol. 69(C), pages 479-487.
    2. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moreno-Sader, K. & Meramo-Hurtado, S.I. & González-Delgado, A.D., 2019. "Computer-aided environmental and exergy analysis as decision-making tools for selecting bio-oil feedstocks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 42-57.
    2. Olga Orynycz & Antoni Świć, 2018. "The Effects of Material’s Transport on Various Steps of Production System on Energetic Efficiency of Biodiesel Production," Sustainability, MDPI, vol. 10(8), pages 1-12, August.
    3. Sinan Erdogan & Cenk Sayin, 2018. "Selection of the Most Suitable Alternative Fuel Depending on the Fuel Characteristics and Price by the Hybrid MCDM Method," Sustainability, MDPI, vol. 10(5), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    3. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    4. Yannan Zhou & Jixia Huang & Mingxiang Huang & Yicheng Lin, 2019. "The Driving Forces of Carbon Dioxide Equivalent Emissions Have Spatial Spillover Effects in Inner Mongolia," IJERPH, MDPI, vol. 16(10), pages 1-14, May.
    5. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    6. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    7. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    8. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    9. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    10. Mohammed S. Almuhayawi & Elhagag A. Hassan & Saad Almasaudi & Nidal Zabermawi & Esam I. Azhar & Azhar Najjar & Khalil Alkuwaity & Turki S. Abujamel & Turki Alamri & Steve Harakeh, 2023. "Biodiesel Production through Rhodotorula toruloides Lipids and Utilization of De-Oiled Biomass for Congo Red Removal," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    11. Jawaharraj, Kalimuthu & Karpagam, Rathinasamy & Ashokkumar, Balasubramaniem & Pratheeba, Chanda Nagarajan & Varalakshmi, Perumal, 2016. "Enhancement of biodiesel potential in cyanobacteria: using agro-industrial wastes for fuel production, properties and acetyl CoA carboxylase D (accD) gene expression of Synechocystis sp.NN," Renewable Energy, Elsevier, vol. 98(C), pages 72-77.
    12. Amal Elasri-Ejjaberi & Pilar Aparicio-Chueca & Xavier M. Triadó-Ivern, 2020. "An Analysis of the Determinants of Sport Expenditure in Sports Centers in Spain," Sustainability, MDPI, vol. 12(23), pages 1-13, December.
    13. Trifoi, Ancuţa Roxana & Agachi, Paul Şerban & Pap, Timea, 2016. "Glycerol acetals and ketals as possible diesel additives. A review of their synthesis protocols," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 804-814.
    14. Faisal Mohammad & Mohamed A. Ahmed & Young-Chon Kim, 2021. "Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System," Energies, MDPI, vol. 14(19), pages 1-23, September.
    15. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    17. Zhang, Long & Bai, Wuliyasu, 2021. "Sustainability of crop–based biodiesel for transportation in China: Barrier analysis and life cycle ecological footprint calculations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    18. Gimeno-Frontera, Beatriz & Mainar-Toledo, María Dolores & Sáez de Guinoa, Aitana & Zambrana-Vasquez, David & Zabalza-Bribián, Ignacio, 2018. "Sustainability of non-residential buildings and relevance of main environmental impact contributors' variability. A case study of food retail stores buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 669-681.
    19. Huang, Luling & Nock, Destenie & Cong, Shuchen & Qiu, Yueming (Lucy), 2023. "Inequalities across cooling and heating in households: Energy equity gaps," Energy Policy, Elsevier, vol. 182(C).
    20. Hao Yang & Maoyu Ran & Chaoqun Zhuang, 2022. "Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression," Energies, MDPI, vol. 15(22), pages 1-19, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:127-:d:125898. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.